Automated Client Reporting for Agencies

The economics here are better than most automation projects, because the cost scales with the number of report templates rather than the number of clients. One template serving forty clients costs roughly what it costs serving four. For an agency doing manual reporting, the hours saved are proportional to your client count while the build is not.

In practice most agencies need two or three templates — one per service line, perhaps a longer quarterly version — rather than one per client. Client-specific variation is handled by configuration: which channels they run, which metrics they care about, their goals, their branding. We would resist building a bespoke report per client, since that recreates the maintenance problem in code.

Everything carries your branding and nothing carries ours. These go to your clients under your name, which is the default rather than an option.

The first is data freshness. Advertising platforms restate recent figures. A conversion attributed today may be credited to a click from several days ago, so yesterday's numbers continue to change for some days afterwards. A report generated on the first of the month using data up to the last day will show figures that no longer match when a client checks the platform a week later — and the client will assume your report was wrong. We handle this by either reporting on a period far enough back to have settled, or stating the data-as-at time on the report itself. Either is fine; silently ignoring it is not.

The second is partial data. If one platform's API fails or returns an incomplete period, the report must not go out with a gap or a zero. A client report showing zero spend because a request timed out does more damage than a report arriving a day late. So every report is validated before sending — period completeness, components summing to totals, and implausible movements flagged — and a failed check holds the report and tells you which source failed.

The narrative is written only around figures that have passed those checks. Every number in the report is computed in code; the model is given those computed numbers and writes the explanation. It has no route to produce a figure of its own, which is the architectural answer to whether it can invent results.